How Early Adopters Used Generative AI Worldwide: Variation by Country Income and Language
This study provides insights into the global adoption patterns of generative AI, highlighting potential digital divide implications for policymakers and AI developers.
This paper analyzes how early adopters worldwide used a free AI chatbot, finding that schooling was the most common use, especially in low-income countries, while leisure use correlated with higher country income. It also observed that English interactions were overrepresented in regions with less-supported native languages.
AI is being used by people globally, but not everyone is using it in the same ways. Using a large-scale dataset of anonymized, de-identified, and privacy-scrubbed interactions with a widely available and free AI chatbot, we empirically characterize differences in early adopters' usage across countries. Schooling is the most common domain of use in most countries, particularly low-income countries, with a strong inverse association evident between schooling and country-level GDP. Leisure-related use, by contrast, is positively associated with country-level income. Language, we find, also shapes use: English-language interactions are overrepresented in places where the predominant languages were not well-served by existing models during the period of the study. Improving performance across languages may be a key factor, our work suggests, in whether this technology expands digital divides or enables leapfrogging.